As [1]} noted, these advantages make BART an attractive choice for estimating heterogeneous treatment effects.
To wit, conditional average treatment effects may be non-linear and may depend on complicated interactions between confounders, covariates, and treatment.
Correctly specifying such non-linearity and interactions is difficult — if not impossible — in most practical settings, making BART particularly compelling.
Given observed triplets \((\mathbf {x}, y, z)\)  of covariates \(\mathbf {x}\) , outcome \(y\) , and treatment indicator \(z,\)  [1]} used BART to obtain an estimate \(\hat{f}(\mathbf {x}, z)\)  of the response surface \(\mathbb {E}[y \vert \mathbf {x}, z]\)  before estimating treatment effects as \(\hat{f}(\mathbf {x},1) - \hat{f}(\mathbf {x},0).\) 
In past iterations of the ACIC Data Challenge, BART-based models have consistently ranked among the top-performing methods.
One method in particular — [3]}'s Bayesian causal forests (BCF) model — stands out as particularly adept at estimating heterogeneous treatment effects.
In this paper, we describe our experience deploying BCF to the 2022 ACIC Data Challenge.
